Simulation systems typically support decision making, allowing a specialist user to observe one or more sequential sub-processes without actually performing the related activities in the real world. Supply chain management is one example of a process comprised of sequential sub-processes that uses simulation.
Simulation applications are typically characterized by features of the domain that are shared by the sub-processes and drive queries of the user. The features define the relevant aspects for the execution of sub-processes, such as the available resources, and the relevant information for decision making, such as the time to complete each activity. Typically, the level of detail of the entire simulation process is chosen based on its target features (i.e., specific simulation behaviors that can be quantified and are important for analysis and decision making).
The sub-processes within the entire process are often managed and operated by different agents, using distinct tools and information systems. In addition, multiple simulation models are typically built over time, for different goals and covering different sub-processes. The execution of these models generates multiple datasets, each representing the workings of part of the complete process under a specific scenario. Thus, there are multiple, heterogeneous, data sources, such as simulation results, or logs with observations from the real world, providing information on how each of these sub-processes operates.
U.S. patent application Ser. No. 15/223,472, filed Jul. 29, 2016, entitled “Automatic Combination of Sub-Process Simulation Results and Heterogeneous Data Sources,” incorporated by reference herein, provides a method for generating a probability distribution function of a target feature of the domain based on these heterogeneous results, without having to create and execute a unified simulation. By doing this, results can be obtained when such unified simulation is not available and, even when it is available, it is possible to approximate results accurately and much more quickly than executing a complete simulation.
A need exists for improved techniques for composing probability distribution functions of a target feature from simulation results and real-world observations.